Journal article
A New Osteophyte Segmentation Algorithm Using the Partial Shape Model and Its Applications to Rabbit Femur Anterior Cruciate Ligament Transection via Micro-CT Imaging
IEEE transactions on biomedical engineering, Vol.58(8), pp.2212-2227
08/2011
DOI: 10.1109/TBME.2011.2129519
PMCID: PMC4910393
PMID: 21421428
Abstract
Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion at a deteriorating joint. Detection and quantification of osteophytes from computed tomography (CT) images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to distinguish between osteophytes and healthy bones using simple thresholding or edge/texture features due to the similarity of their material composition. In this paper, we present a new method primarily based on the active shape model (ASM) to solve this problem and evaluate its application to the anterior cruciate ligament transaction (ACLT) rabbit femur model via micro-CT imaging. The common idea behind most ASM-based segmentation methods is to first build a parametric shape model from a training dataset and then apply the model to find a shape instance in a target image. A common challenge with such approaches is that a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method and eventually leading to suboptimum segmentations. This difficulty is overcome using a new partial-ASM method that uses bone shape over healthy regions and extrapolates it over the diseased region according to the underlying shape model. Finally, osteophytes are segmented by subtracting partial-ASM-derived shape from the overall diseased shape. Also, a new semiautomatic method is presented in this paper for efficiently building a 3-D shape model for an anatomic region using manual reference of a few anatomically defined fiducial landmarks that are highly reproducible on individuals. Accuracy of the method has been examined on simulated phantoms while reproducibility and sensitivity have been evaluated on micro-CT images of 2-, 4- and 8-week post-ACLT and sham-treated rabbit femurs. Experimental results have shown that the method is highly accurate ( R \bm 2 =0.99), reproducible (ICC = 0.97), and sensitive in detecting disease progression (p values: 0.065, 0.001, and <;0.001 for 2 weeks versus 4 weeks, 4 weeks versus 8 weeks, and 2 weeks versus 8 weeks, respectively).
Details
- Title: Subtitle
- A New Osteophyte Segmentation Algorithm Using the Partial Shape Model and Its Applications to Rabbit Femur Anterior Cruciate Ligament Transection via Micro-CT Imaging
- Creators
- P. K Saha - Department of Electrical and Computer Engineering and the Department of Radiology, the University of Iowa, Iowa City, USAG Liang - Department of Electrical and Computer Engineering, the University of Iowa, Iowa City, USAJ. M Elkins - Department of Orthopaedic Surgery , the University of Iowa, Iowa City, USAA Coimbra - Imaging, Merck Research Laboratories, West Point, USAL. T Duong - Bone Biology, Merck Research Laboratories, West Point, USAD. S Williams - Imaging, Merck Research Laboratories, West Point, USAM Sonka - Department of Electrical and Computer Engineering, the University of Iowa, Iowa City, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on biomedical engineering, Vol.58(8), pp.2212-2227
- DOI
- 10.1109/TBME.2011.2129519
- PMID
- 21421428
- PMCID
- PMC4910393
- NLM abbreviation
- IEEE Trans Biomed Eng
- ISSN
- 0018-9294
- eISSN
- 1558-2531
- Publisher
- IEEE
- Language
- English
- Date published
- 08/2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Orthopedics and Rehabilitation; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984046803702771
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